Bedside use of artificial intelligence (AI) platforms is increasingly common. Busy clinicians may welcome these generative AI (Gen AI) tools, which have the potential to streamline many time-consuming tasks and aid in patient care. Trainees may find them useful to quickly evaluate complex medical information. Acceptance of bedside Gen AI tools by patients and their families, however, is less clear, and a definitive standard surrounding informed consent has yet to be established. Omission of certain types of information by Gen AI tools, including "small talk," which comprises an essential element of many pediatric clinical interactions, demands attention alongside the tendency of Gen AI tools to fabricate content. Medical students and resident physicians may be early adopters of Gen AI tools and may accept AI-generated statements at face value before having fully developed adequate knowledge and critical skills to independently evaluate their veracity. Despite these challenges, Gen AI tools in the clinical space are here to stay. Safe and effective incorporation of these tools in patient care and medical education must balance a wide range of considerations. In the following Ethics Rounds, stemming from a 2024 Pediatric Academic Society Bioethics Club Meeting session on the use of AI, the commentators draw on their diverse background as pediatric clinicians, bioethicists, and educators to explore these issues.
This article proposes seven questions that clinicians can run through to evaluate any clinical AI tool in the time it takes to read an abstract, alongside a traffic-light schema for matching oversight to risk and a short list of demands clinicians should make of vendors and institutions.
Alaa Abdelqader, M. Alkhateeb, Abdullah Al-Marrawi et al.· Avicenna Journal of Medicine· 0 citations
This commentary offers a framework organized around three fundamental domains of clinical care-information collection, data analysis, and treatment delivery-and describes how the physician's role within each is shifting rather than disappearing.
R. Stefanacci· Journal of The American Geri...· 0 citations
This work aims to give medical educators a practical guide for judging which uses of generative AI support skill formation at each stage of training and which displace it, and proposes a framework built on cognitive displacement, which locates each risk where it has the greatest potential for harm.
Nikhil S. Patel, Andre Kumar, Jeffrey Chi et al.· BMJ digital health & AI· 0 citations
This paradigm shift from misuse to misclassification is not semantic: it offers educators a clear perspective on what to look for, what to assess, and what to intervene on.
Fendi Tsim, A. Gutoreva, A. Weiss et al.· 0 citations
Non-institutional AI use was common among surveyed clinicians and extended across a broad range of work-related activities, including tasks with potential implications for clinical reasoning and patient care.
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